A urinary proteome-based classifier for the early detection of decline in glomerular filtration

Nicotinamide N-Methyl Transferase as a Predictive Marker of Tubular Fibrosis in CKD

Purpose

Chronic kidney disease (CKD) progression is complex. There are not standardized methods for predicting the prognosis of CKD. Nicotinamide N-methyltransferase (NNMT) has been shown to be associated with renal fibrosis. This study aimed to validate NNMT as a prognostic biomarker of progressive CKD.

Patients and Methods

We explored the relationship between NNMT expression and CKD-related outcome variables using the NephroseqV5 and GEO databases. Additionally, a validation set of 37 CKD patients was enrolled to measure the correlation between NNMT expression levels and CKD outcomes. Furthermore, single-cell RNA sequencing data and the Human Protein Atlas were reanalyzed to investigate the expression specificity of NNMT in the kidney. Finally, to detect the status of NNMT expression with tubular fibrosis in vivo, we constructed a unilateral ureteral obstruction (UUO) mouse treated with an NNMT inhibitor.

Results

Analyzing the datasets showed that NNMT was expressed mainly in proximal tubule compartments. And patients with high NNMT expression levels had a significantly lower overall survival rate compared to those with low NNMT expression levels (P = 0.013). NNMT was independent of prognosis factors in the multivariate Cox regression model, and the AUCs for CKD progression at 1, 3, and 5 years were 0.849, 0.775, and 0.877, respectively. Pathway enrichment analysis indicated that NNMT regulates the biological processes of tubulointerstitial fibrosis (TIF). In the validation group, NNMT levels were significantly higher in the CKD group combined with interstitial fibrosis. In vivo, NNMT was a high expression in the UUO group, peaking at postoperative day 21. Treatment with an NNMT inhibitor improved renal tubular interstitial fibrosis, and expression levels of FN, α-SMA, VIM, and TGF-β1 were decreased compared with UUO (P < 0.05).

Conclusion

NNMT was expressed mainly in tubular renal compartments, and associated with CKD prognosis. It holds potential as a diagnostic biomarker for tubular fibrosis in CKD.

Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients

INTRODUCTION

Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β-CrossLaps (CTX), a C-terminal collagen α-1(I) chain (COL1A1) telopeptide. We investigated the low-molecular-weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation.

METHODS

Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry.

RESULTS

Eighty-two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline.

DISCUSSION

This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Molecular Mapping of Urinary Complement Peptides in Kidney Diseases

Defective complement activation has been associated with various types of kidney disease. This led to the hypothesis that specific urine complement fragments may be associated with kidney disease etiologies, and disease progression may be reflected by changes in these complement fragments. We investigated the occurrence of complement fragments in urine, their association with kidney function and disease etiology in 16,027 subjects, using mass spectrometry based peptidomics data from the Human Urinary Proteome/Peptidome Database. Twenty-three different urinary peptides originating from complement proteins C3, C4 and factor B (CFB) could be identified. Most C3-derived peptides showed inverse association with estimated glomerular filtration rate (eGFR), while the majority of peptides derived from CFB demonstrated positive association with eGFR. Several peptides derived from the complement proteins C3, C4 and CFB were found significantly associated with specific kidney disease etiologies. These peptides may depict disease-specific complement activation and could serve as non-invasive biomarkers to support development of complement interventions through assessing complement activity for patients’ stratification and monitoring of drug impact. Further investigation of these complement peptides may provide additional insight into disease pathophysiology and could possibly guide therapeutic decisions, especially when targeting complement factors.

Reproducibility Evaluation of Urinary Peptide Detection Using CE-MS

In recent years, capillary electrophoresis coupled to mass spectrometry (CE-MS) has been increasingly applied in clinical research especially in the context of chronic and age-associated diseases, such as chronic kidney disease, heart failure and cancer. Biomarkers identified using this technique are already used for diagnosis, prognosis and monitoring of these complex diseases, as well as patient stratification in clinical trials. CE-MS allows for a comprehensive assessment of small molecular weight proteins and peptides (<20 kDa) through the combination of the high resolution and reproducibility of CE and the distinct sensitivity of MS, in a high-throughput system. In this study we assessed CE-MS analytical performance with regards to its inter- and intra-day reproducibility, variability and efficiency in peptide detection, along with a characterization of the urinary peptidome content. To this end, CE-MS performance was evaluated based on 72 measurements of a standard urine sample (60 for inter- and 12 for intra-day assessment) analyzed during the second quarter of 2021. Analysis was performed per run, per peptide, as well as at the level of biomarker panels. The obtained datasets showed high correlation between the different runs, low variation of the ten highest average individual log2 signal intensities (coefficient of variation, CV < 10%) and very low variation of biomarker panels applied (CV close to 1%). The findings of the study support the analytical performance of CE-MS, underlining its value for clinical application.

Urinary peptidomic profiles to address age-related disabilities: a prospective population study

BACKGROUND

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 called for innovation in addressing age-related disabilities. Our study aimed to identify and validate a urinary peptidomic profile (UPP) differentiating healthy from unhealthy ageing in the general population, to test the UPP predictor in independent patient cohorts, and to search for targetable molecular pathways underlying age-related chronic diseases.

METHODS

In this prospective population study, we used data from participants in the Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO), done in northern Belgium from 1985 to 2019, and invited participants to a follow-up examination in 2005-10. Participants were eligible if their address was within 15 km of the examination centre and if they had not withdrawn consent in any of the previous examination cycles (1985-2004). All participants (2005-10) were also invited to an additional follow-up examination in 2009-13. Participants who took part in both the 2005-10 follow-up examination and in the additional 2009-13 follow-up visit constituted the derivation dataset, which included their 2005-10 data, and the time-shifted internal validation dataset, which included their 2009-13 data. The remaining participants who only had 2005-10 data constituted the synchronous internal validation dataset. Participants were excluded from analyses if they were incapacitated, had not undergone UPP, or had either missing or outlying (three SDs greater than the mean of all consenting participants) values of body-mass index, plasma glucose, or serum creatinine. The UPP was assessed by capillary electrophoresis coupled with mass spectrometry. The multidimensional UPP signature reflecting ageing was generated from the derivation dataset and validated in the time-shifted internal validation dataset and the synchronous validation dataset. It was further validated in patients with diabetes, COVID-19, or chronic kidney disease (CKD). In FLEMENGHO, the mortality endpoints were all-cause, cardiovascular, and non-cardiovascular mortality; other endpoints were fatal or non-fatal cancer and musculoskeletal disorders. Molecular pathway exploration was done using the Reactome and Kyoto Encyclopedia of Genes and Genomes databases.

FINDINGS

778 individuals (395 [51%] women and 383 [49%] men; aged 16·2-82·1 years; mean age 50·9 years [SD 15·8]) from the FLEMENGHO cohort had a follow-up examination between 2005 and 2010, of whom 559 participants had a further follow-up from Oct 28, 2009, to March 19, 2013, and made up the derivation (2005-10) and time-shifted internal validation (2009-13) datasets. 219 were examined once and constituted the synchronous internal validation dataset (2005-10). With correction for multiple testing and multivariable adjustment, chronological age was associated with 210 sequenced peptides mainly showing downregulation of collagen fragments. The trained model relating chronological age to UPP, derived by elastic net regression, included 54 peptides from 17 proteins. The UPP-age prediction model explained 76·3% (r=0·87) of chronological age in the derivation dataset, 54·4% (r=0·74) in the time-shifted validation dataset, and 65·3% (r=0·81) in the synchronous internal validation dataset. Compared with chronological age, the predicted UPP-age was greater in patients with diabetes (chronological age 50·8 years [SE 0·37] vs UPP-age 56·9 years [0·30]), COVID‑19 (53·2 years [1·80] vs 58·5 years [1·67]), or CKD (54·6 years [0·97] vs 62·3 years [0·85]; all p<0·0001). In the FLEMENGHO cohort, independent of chronological age, UPP-age was significantly associated with various risk markers related to cardiovascular, metabolic, and renal disease, inflammation, and medication use. Over a median of 12·4 years (IQR 10·8-13·2), total mortality, cardiovascular mortality, and osteoporosis in the population was associated with UPP-age independent of chronological age, with hazard ratios per 10 year increase in UPP-age of 1·54 (95% CI 1·22-1·95) for total mortality, 1·72 (1·20-2·47) for cardiovascular mortality, and 1·40 (1·06-1·85) for osteoporosis and fractures. The most relevant molecular pathways informed by the proteins involved deregulation of collagen biology and extracellular matrix maintenance.

INTERPRETATION

The UPP signature indicative of ageing reflects fibrosis and extracellular matrix remodelling and was associated with risk factors and adverse health outcomes in the population and with accelerated ageing in patients. Innovation in addressing disability should shift focus from the ontology of diseases to shared disease mechanisms, in particular ageing-related fibrotic degeneration.

FUNDING

European Research Council, Ministry of the Flemish Community, OMRON Healthcare.

Novel biomarkers of diabetic kidney disease: current status and potential clinical application

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease (ESRD). Although both albuminuria and glomerular filtration rate (GFR) are well-established diagnostic/prognostic biomarkers of DKD, they have important limitations. There is, thus, increasing quest to find novel biomarkers to identify the disease in an early stage and to improve risk stratification. In this review, we will outline the major pitfalls of currently available markers, describe promising novel biomarkers, and discuss their potential clinical relevance. In particular, we will focus on the importance of recent advancements in multi-omic technologies in the discovery of new DKD biomarkers. In addition, we will provide an update on new emerging approaches to explore renal function and structure, using functional tests and imaging.

One-year estimated glomerular filtration rate decline as a risk factor of cardiovascular and renal end-points in high-risk Japanese patients

AIMS/INTRODUCTION

As estimated glomerular filtration rate (eGFR) progression might correlate with cardiovascular prognosis, the correlation between 1-year decline in eGFR and cardiovascular incidences and renal outcome was investigated.

MATERIALS AND METHODS

The 1-year percentage decline in eGFR at the first observation year was calculated in a cohort of the standard versus intEnsive statin therapy for hypercholesteroleMic Patients with diAbetic retinopaTHY (EMPATHY) trial participants. The primary end-point was the composite cardiovascular end-point including the renal end-point. The associations between the incidence of each end-point and clinical markers were analyzed using the Cox proportional hazards regression model.

RESULTS

A total of 4,461 patients were analyzed. The mean observation period was 765.3 ± 363.1 days. The best cut-off value of 1-year eGFR decline was 0.099 in the first year for renal end-point prediction by receiver operating characteristic curve analysis. The area under the curve of the model including the 1-year eGFR decline of the first year was significantly larger than the model without it (0.943, 95% confidence interval 0.915-0.971 to 0.967, 95% confidence interval 0.950-0.983, P = 0.019). Primary end-point incidences and the renal end-point were much higher in rapid eGFR decliners compared with non-decliners (P < 0.0001). The cardiovascular end-point incidence, except for the renal end-point, was not different between the groups. According to Cox regression analysis, 1-year eGFR decline during the first year was a significant risk factor for the end-points, including the renal end-point, independent of albuminuria and eGFR at baseline.

CONCLUSIONS

The 1-year eGFR decline rate provided useful information for cardiovascular end-point predictions, including the renal end-point, in addition to the conventional risk factors.

A urinary peptidomic profile predicts outcome in SARS-CoV-2-infected patients

BACKGROUND

COVID-19 prediction models based on clinical characteristics, routine biochemistry and imaging, have been developed, but little is known on proteomic markers reflecting the molecular pathophysiology of disease progression.

METHODS

The multicentre (six European study sites) Prospective Validation of a Proteomic Urine Test for Early and Accurate Prognosis of Critical Course Complications in Patients with SARS-CoV-2 Infection Study (Crit-COV-U) is recruiting consecutive patients (≥ 18 years) with PCR-confirmed SARS-CoV-2 infection. A urinary proteomic biomarker (COV50) developed by capillary-electrophoresis-mass spectrometry (CE-MS) technology, comprising 50 sequenced peptides and identifying the parental proteins, was evaluated in 228 patients (derivation cohort) with replication in 99 patients (validation cohort). Death and progression along the World Health Organization (WHO) Clinical Progression Scale were assessed up to 21 days after the initial PCR test. Statistical methods included logistic regression, receiver operating curve (ROC) analysis and comparison of the area under the curve (AUC).

FINDINGS

In the derivation cohort, 23 patients died, and 48 developed worse WHO scores. The odds ratios (OR) for death per 1 standard deviation (SD) increment in COV50 were 3·52 (95% CI, 2·02-6·13, p <0·0001) unadjusted and 2·73 (1·25-5·95, p = 0·012) adjusted for sex, age, baseline WHO score, body mass index (BMI) and comorbidities. For WHO scale progression, the corresponding OR were 2·63 (1·80-3·85, p<0·0001) and 3·38 (1·85-6·17, p<0·0001), respectively. The area under the curve (AUC) for COV50 as a continuously distributed variable was 0·80 (0·72-0·88) for mortality and 0·74 (0·66-0·81) for worsening WHO score. The optimised COV50 thresholds for mortality and worsening WHO score were 0·47 and 0·04 with sensitivity/specificity of 87·0 (74·6%) and 77·1 (63·9%), respectively. On top of covariates, COV50 improved the AUC, albeit borderline for death, from 0·78 to 0·82 (p = 0·11) and 0·84 (p = 0·052) for mortality and from 0·68 to 0·78 (p = 0·0097) and 0·75 (p = 0·021) for worsening WHO score. The validation cohort findings were confirmatory.

INTERPRETATION

This first CRIT-COV-U report proves the concept that urinary proteomic profiling generates biomarkers indicating adverse COVID-19 outcomes, even at an early disease stage, including WHO stages 1-3. These findings need to be consolidated in an upcoming final dataset.

FUNDING

The German Federal Ministry of Health funded the study.

A systematic review and participant-level meta-analysis found little association of retinal microvascular caliber with reduced kidney function

Previously, variation in retinal vascular caliber has been reported in association with chronic kidney disease (CKD) but findings remain inconsistent. To help clarify this we conducted individual participant data meta-analysis and aggregate data meta-analysis on summary estimates to evaluate cross-sectional associations between retinal vascular caliber and CKD. A systematic review was performed using Medline and EMBASE for articles published until October 2018. The aggregate analysis used a two-stage approach combining summary estimates from eleven studies (44,803 patients) while the individual participant analysis used a one-stage approach combining raw data from nine studies (33,222 patients). CKD stages 3-5 was defined as an estimated glomerular filtration rate under 60 mL/min/1.73m2. Retinal arteriolar and venular caliber (central retinal arteriolar and venular equivalent) were assessed from retinal photographs using computer-assisted methods. Logistic regression estimated relative risk of CKD stages 3-5 associated with a 20 μm decrease (approximately one standard deviation) in central retinal arteriolar and venular equivalent. Prevalence of CKD stages 3-5 was 11.2% of 33,222 and 11.3% of 44,803 patients in the individual participant and aggregate data analysis, respectively. No significant associations were detected in adjusted analyses between central retinal arteriolar and venular equivalent and CKD stages 3-5 in the aggregate analysis for central retinal arteriolar relative risk (0.98, 95% confidence interval 0.94-1.03); venular equivalent (0.99, 0.95-1.04) or individual participant central retinal arteriolar (0.99, 0.95-1.04) or venular equivalent (1.01, 0.97-1.05). Thus, meta-analysis provided little evidence to suggest that cross sectional direct measurements of retinal vascular caliber was associated with CKD stages 3-5 in the general population. Hence, meta-analyses of longitudinal studies evaluating the association between retinal parameters and CKD stages 3-5 may be warranted.

Development of a Biomarker Panel to Distinguish Risk of Progressive Chronic Kidney Disease

Chronic kidney disease (CKD) patients typically progress to kidney failure, but the rate of progression differs per patient or may not occur at all. Current CKD screening methods are sub-optimal at predicting progressive kidney function decline. This investigation develops a model for predicting progressive CKD based on a panel of biomarkers representing the pathophysiological processes of CKD, kidney function, and common CKD comorbidities. Two patient cohorts are utilised: The CKD Queensland Registry (n = 418), termed the Biomarker Discovery cohort; and the CKD Biobank (n = 62), termed the Predictive Model cohort. Progression status is assigned with a composite outcome of a ≥30% decline in eGFR from baseline, initiation of dialysis, or kidney transplantation. Baseline biomarker measurements are compared between progressive and non-progressive patients via logistic regression. In the Biomarker Discovery cohort, 13 biomarkers differed significantly between progressive and non-progressive patients, while 10 differed in the Predictive Model cohort. From this, a predictive model, based on a biomarker panel of serum creatinine, osteopontin, tryptase, urea, and eGFR, was calculated via linear discriminant analysis. This model has an accuracy of 84.3% when predicting future progressive CKD at baseline, greater than eGFR (66.1%), sCr (67.7%), albuminuria (53.2%), or albumin-creatinine ratio (53.2%).

Urinary fetuin-A peptides as a new marker for impaired kidney function in patients with type 2 diabetes

Background

The hepatokine fetuin-A, released by the human liver, promotes pro-inflammatory effects of perivascular fat. The involvement of inflammation in type 2 diabetes mellitus (T2DM) can affect the kidney and contribute to the development of diabetic kidney disease. Therefore we examined the association of urinary fetuin-A protein fragments with renal damage in T2DM patients.

Methods

Urinary peptides of 1491 individuals using proteome data available from the human urine proteome database were analysed. Prediction of proteases involved in urinary peptide generation was performed using the Proteasix tool.

Results

We identified 14 different urinary protein fragments that belong to the region of the connecting peptide (amino acid 301–339) of the total fetuin-A protein. Calpains (CAPN1 and CAPN2), matrix metalloproteinase and pepsin A-3 were identified as potential proteases that were partially confirmed by previous in vitro studies. Combined fetuin-A peptides (mean of amplitudes) were significantly increased in T2DM patients with kidney disease and to a lesser extent with cardiovascular risk. Furthermore, fetuin-A peptide levels displayed a significant negative correlation with baseline estimated glomerular filtration rate (eGFR) values (r = −0.316, P < 0.0001) and with the slope (%) of eGFR per year (r = −0.096, P = 0.023). A multiple regression model including fetuin-A peptide and albuminuria resulted in a significantly improved correlation with eGFR (r = −0.354, P < 0.0001) compared with albuminuria, indicating an added value of this novel biomarker.

Conclusions

The urinary proteome analysis demonstrated the association of fetuin-A peptides with impaired kidney function in T2DM patients. Furthermore, fetuin-A peptides displayed early signs of kidney damage before albuminuria appeared and therefore can be used as markers for kidney disease detection.

High fractional excretion of glycation adducts is associated with subsequent early decline in renal function in type 1 diabetes

Increased protein glycation, oxidation and nitration is linked to the development of diabetic nephropathy. We reported levels of serum protein glycation, oxidation and nitration and related hydrolysis products, glycation, oxidation and nitration free adducts in patients with type 1 diabetes (T1DM) during onset of microalbuminuria (MA) from the First Joslin Kidney Study, a prospective case-control study of patients with T1DM with and without early decline in GFR. Herein we report urinary excretion of the latter analytes and related fractional excretion values, exploring the link to MA and early decline in GFR. We recruited patients with T1DM and normoalbuminuria (NA) (n = 30) or new onset MA with and without early GFR decline (n = 22 and 33, respectively) for this study. We determined urinary protein glycation, oxidation and nitration free adducts by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry (LC-MS/MS) and deduced fractional excretion using reported plasma levels and urinary and plasma creatinine estimates. We found urinary excretion of pentosidine was increased ca. twofold in patients with MA, compared to normoalbuminuria (0.0442 vs 0.0103 nmol/mg creatinine, P < 0.0001), and increased ca. threefold in patients with early decline in GFR, compared to patients with stable GFR (0.0561 vs 0.0176 nmol/mg creatinine, P < 0.01). Urinary excretion of all other analytes was unchanged between the study groups. Remarkably, fractional excretions of 6 lysine and arginine-derived glycation free adducts were higher in patients with early decline in GFR, compared to those with stable GFR. Impaired tubular reuptake of glycation free adducts by lysine and arginine transporter proteins in patients with early GFR decline is likely involved. We conclude that higher fractional excretions of glycation adducts are potential biomarkers for early GFR decline in T1DM and MA. Measurement of these analytes could provide the basis for identifying patients at risk of early decline in renal function to target and intensify renoprotective treatment.

Proteomics-Based Machine Learning Approach as an Alternative to Conventional Biomarkers for Differential Diagnosis of Chronic Kidney Diseases

Diabetic nephropathy, hypertension, and glomerulonephritis are the most common causes of chronic kidney diseases (CKD). Since CKD of various origins may not become apparent until kidney function is significantly impaired, a differential diagnosis and an appropriate treatment are needed at the very early stages. Conventional biomarkers may not have sufficient separation capabilities, while a full-proteomic approach may be used for these purposes. In the current study, several machine learning algorithms were examined for the differential diagnosis of CKD of three origins. The tested dataset was based on whole proteomic data obtained after the mass spectrometric analysis of plasma and urine samples of 34 CKD patients and the use of label-free quantification approach. The k-nearest-neighbors algorithm showed the possibility of separation of a healthy group from renal patients in general by proteomics data of plasma with high confidence (97.8%). This algorithm has also be proven to be the best of the three tested for distinguishing the groups of patients with diabetic nephropathy and glomerulonephritis according to proteomics data of plasma (96.3% of correct decisions). The group of hypertensive nephropathy could not be reliably separated according to plasma data, whereas analysis of entire proteomics data of urine did not allow differentiating the three diseases. Nevertheless, the group of hypertensive nephropathy was reliably separated from all other renal patients using the k-nearest-neighbors classifier “one against all” with 100% of accuracy by urine proteome data. The tested algorithms show good abilities to differentiate the various groups across proteomic data sets, which may help to avoid invasive intervention for the verification of the glomerulonephritis subtypes, as well as to differentiate hypertensive and diabetic nephropathy in the early stages based not on individual biomarkers, but on the whole proteomic composition of urine and blood.

Urinary peptidomic biomarkers of renal function in heart transplant recipients

Background

Chronic kidney disease (CKD) is common in patients after heart transplantation (HTx). We assessed whether in HTx recipients the proteomic urinary classifier CKD273 or sequenced urinary peptides revealing the parental proteins correlated with the estimated glomerular filtration rate (eGFR).

Methods

In 368 HTx patients, we measured the urinary peptidome and analysed CKD273 and 48 urinary peptides with a detectable signal in >95% of participants. After 9.1 months (median), eGFR and the urinary biomarkers were reassessed.

Results

In multivariable Bonferroni-corrected analyses of the baseline data, a 1-SD increase in CKD273 was associated with a 11.4 [95% confidence interval (CI) 7.25–15.5] mL/min/1.73 m² lower eGFR and an odds ratio of 2.63 (1.56–4.46) for having eGFR <60 mL/min/1.73 m². While relating eGFR category at follow-up to baseline urinary biomarkers, CKD273 had higher (P = 0.007) area under the curve (0.75; 95% CI 0.70–0.80) than 24-h proteinuria (0.64; 95% CI 0.58–0.69), but additional adjustment for baseline eGFR removed significance of both biomarkers. In partial least squares analysis, the strongest correlates of the multivariable-adjusted baseline eGFR were fragments of collagen I (positive) and the mucin-1 subunit α (inverse). Associations between the changes in eGFR and the urinary markers were inverse for CKD273 and mucin-1 and positive for urinary collagen I.

Conclusions

With the exception of baseline eGFR, CKD273 was more closer associated with imminent renal dysfunction than 24-h proteinuria. Fragments of collagen I and mucin-1—respectively, positively and inversely associated with eGFR and change in eGFR—are single-peptide markers associated with renal dysfunction.

A single-center study to evaluate the efficacy of a fetal urine peptide signature predicting postnatal renal outcome in fetuses with posterior urethral valves

BACKGROUND

Posterior urethral valves (PUVs) account for 17% of pediatric renal failure. The management of pregnancies involving fetuses with PUV is hampered by the fact that current clinical parameters obtained from fetal ultrasound and/or fetal urine biochemistry are insufficient to predict postnatal renal function. We previously have developed a fetal urine peptide signature (12PUV) that predicted with high precision postnatal renal failure at 2 years of age in fetuses with PUV. Here, we evaluated the accuracy of this signature to predict postnatal renal outcome in fetuses with PUV in an independent single-center study.

METHODS

Thirty-three women carrying fetuses with suspected PUV were included. Twenty-five fetuses received vesicoamniotic shunts during pregnancy. PUV was confirmed postnatally in 23 patients. Of those 23 fetuses, 2 were lost in follow-up. Four and 3 patients died in the pre- and perinatal periods, respectively. Follow-up renal function at 6 months of age was obtained for the remaining 14 patients. The primary outcome was early renal failure, defined by an eGFR < 60 mL/min/1.73 m² before 6 months of age or pre- or perinatal death.

RESULTS

The peptide signature predicted postnatal renal outcome in postnatally confirmed PUV fetuses with an AUC of 0.94 (95%CI 0.74-1.0) and an accuracy of 90% (95%CI 78-100). The signature predicted postnatal renal outcome for the suspected PUV cases with an AUC of 0.89 (95%CI 0.72-0.97) and an accuracy of 84% (95%CI 71-97).

CONCLUSIONS

This single-center study confirms the predictive power of the previously identified 12PUV fetal urinary peptide signature.

Urinary peptidomics and bioinformatics for the detection of diabetic kidney disease

The aim of this study was to establish a peptidomic profile based on LC-MS/MS and random forest (RF) algorithm to distinguish the urinary peptidomic scenario of type 2 diabetes mellitus (T2DM) patients with different stages of diabetic kidney disease (DKD). Urine from 60 T2DM patients was collected: 22 normal (stage A1), 18 moderately increased (stage A2) and 20 severely increased (stage A3) albuminuria. A total of 1080 naturally occurring peptides were detected, which resulted in the identification of a total of 100 proteins, irrespective of the patients’ renal status. The classification accuracy showed that the most severe DKD (A3) presented a distinct urinary peptidomic pattern. Estimates for peptide importance assessed during RF model training included multiple fragments of collagen and alpha-1 antitrypsin, previously associated to DKD. Proteasix tool predicted 48 proteases potentially involved in the generation of the 60 most important peptides identified in the urine of DM patients, including metallopeptidases, cathepsins, and calpains. Collectively, our study lightened some biomarkers possibly involved in the pathogenic mechanisms of DKD, suggesting that peptidomics is a valuable tool for identifying the molecular mechanisms underpinning the disease and thus novel therapeutic targets.

Urinary Peptidomic Biomarkers in Kidney Diseases

In order to effectively develop personalized medicine for kidney diseases we urgently need to develop highly accurate biomarkers for use in the clinic, since current biomarkers of kidney damage (changes in serum creatinine and/or urine albumin excretion) apply to a later stage of disease, lack accuracy, and are not connected with molecular pathophysiology. Analysis of urine peptide content (urinary peptidomics) has emerged as one of the most attractive areas in disease biomarker discovery. Urinary peptidome analysis allows the detection of short and long-term physiological or pathological changes occurring within the kidney. Urinary peptidomics has been applied extensively for several years now in renal patients, and may greatly improve kidney disease management by supporting earlier and more accurate detection, prognostic assessment, and prediction of response to treatment. It also promises better understanding of kidney disease pathophysiology, and has been proposed as a “liquid biopsy” to discriminate various types of renal disorders. Furthermore, proteins being the major drug targets, peptidome analysis may allow one to evaluate the effects of therapies at the protein signaling pathway level. We here review the most recent findings on urinary peptidomics in the setting of the most common kidney diseases.

Clinical Proteomics in Kidney Disease: From Discovery to Clinical Application

Proteome analysis has been applied in multiple studies in the context of chronic kidney disease, aiming at improving our knowledge on the molecular pathophysiology of the disease. The approach is generally based on the hypothesis that proteins are key in maintaining kidney function, and disease is a clinical consequence of a significant change of the protein level. Knowledge on critical proteins and their alteration in disease should in turn enable identification of ideal biomarkers that could guide patient management. In addition, all drugs currently employed target proteins. Hence, proteome analysis also promises to enable identifying the best suited therapeutic target, and, in combination with biomarkers, could be used as the rationale basis for personalized intervention. To assess the current status of proteome analysis in the context of CKD, we present the results of a systematic review, of up-to-date scientific research, and give an outlook on the developments that can be expected in near future. Based on the current literature, proteome analysis has already seen implementation in the management of CKD patients, and it is expected that this approach, also supported by the positive results generated to date, will see advanced high-throughput application.

Identification of serum metabolites associating with chronic kidney disease progression and anti-fibrotic effect of 5-methoxytryptophan

Early detection and accurate monitoring of chronic kidney disease (CKD) could improve care and retard progression to end-stage renal disease. Here, using untargeted metabolomics in 2155 participants including patients with stage 1–5 CKD and healthy controls, we identify five metabolites, including 5-methoxytryptophan (5-MTP), whose levels strongly correlate with clinical markers of kidney disease. 5-MTP levels decrease with progression of CKD, and in mouse kidneys after unilateral ureteral obstruction (UUO). Treatment with 5-MTP ameliorates renal interstitial fibrosis, inhibits IκB/NF-κB signaling, and enhances Keap1/Nrf2 signaling in mice with UUO or ischemia/reperfusion injury, as well as in cultured human kidney cells. Overexpression of tryptophan hydroxylase-1 (TPH-1), an enzyme involved in 5-MTP synthesis, reduces renal injury by attenuating renal inflammation and fibrosis, whereas TPH-1 deficiency exacerbates renal injury and fibrosis by activating NF-κB and inhibiting Nrf2 pathways. Together, our results suggest that TPH-1 may serve as a target in the treatment of CKD.

Accurate monitoring of chronic kidney disease (CKD) progression is essential for efficient disease management. Here Chen et al. identify five serum metabolites in patients with stage 1–5 CKD whose levels associate with disease progression, and find that 5-methoxytryptophan and its regulatory enzyme TPH-1 exert anti-fibrotic effects in mouse models of kidney injury.

Novel Urinary Biomarkers For Improved Prediction Of Progressive Egfr Loss In Early Chronic Kidney Disease Stages And In High Risk Individuals Without Chronic Kidney Disease

Chronic kidney disease is associated with increased risk of CKD progression and death. Therapeutic approaches to limit progression are limited. Developing tools for the early identification of those individuals most likely to progress will allow enriching clinical trials in high risk early CKD patients. The CKD273 classifier is a panel of 273 urinary peptides that enables early detection of CKD and prognosis of progression. We have generated urine capillary electrophoresis-mass spectrometry-based peptidomics CKD273 subclassifiers specific for CKD stages to allow the early identification of patients at high risk of CKD progression. In the validation cohort, the CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting rapid loss of eGFR in individuals with baseline eGFR > 60 ml/min/1.73 m². In individuals with eGFR > 60 ml/min/1.73 m² and albuminuria <30 mg/day, the CKD273 subclassifiers predicted rapid eGFR loss with AUC ranging from 0.797 (0.743-0.844) to 0.736 (0.689-0.780). The association between CKD273 subclassifiers and rapid progression remained significant after adjustment for age, sex, albuminuria, DM, baseline eGFR, and systolic blood pressure. Urinary peptidomics CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting the risk of rapid CKD progression in individuals with eGFR > 60 ml/min/1.73 m². These CKD273 subclassifiers represented the earliest evidence of rapidly progressive CKD in non-albuminuric individuals with preserved renal function.

Noninvasive diagnosis of chronic kidney diseases using urinary proteome analysis

Background

In spite of its invasive nature and risks, kidney biopsy is currently required for precise diagnosis of many chronic kidney diseases (CKDs). Here, we explored the hypothesis that analysis of the urinary proteome can discriminate different types of CKD irrespective of the underlying mechanism of disease.

Methods

We used data from the proteome analyses of 1180 urine samples from patients with different types of CKD, generated by capillary electrophoresis coupled to mass spectrometry. A set of 706 samples served as the discovery cohort, and 474 samples were used for independent validation. For each CKD type, peptide biomarkers were defined using statistical analysis adjusted for multiple testing. Potential biomarkers of statistical significance were combined in support vector machine (SVM)-based classifiers.

Results

For seven different types of CKD, several potential urinary biomarker peptides (ranging from 116 to 619 peptides) were defined and combined into SVM-based classifiers specific for each CKD. These classifiers were validated in an independent cohort and showed good to excellent accuracy for discrimination of one CKD type from the others (area under the receiver operating characteristic curve ranged from 0.77 to 0.95). Sequence analysis of the biomarkers provided further information that may clarify the underlying pathophysiology.

Conclusions

Our data indicate that urinary proteome analysis has the potential to identify various types of CKD defined by pathological assessment of renal biopsies and current clinical practice in general. Moreover, these approaches may provide information to model molecular changes per CKD.

Biomarkers of diabetic kidney disease

Diabetic kidney disease (DKD) remains one of the leading causes of reduced lifespan in diabetes. The quest for both prognostic and surrogate endpoint biomarkers for advanced DKD and end-stage renal disease has received major investment and interest in recent years. However, at present no novel biomarkers are in routine use in the clinic or in trials. This review focuses on the current status of prognostic biomarkers. First, we emphasise that albuminuria and eGFR, with other routine clinical data, show at least modest prediction of future renal status if properly used. Indeed, a major limitation of many current biomarker studies is that they do not properly evaluate the marginal increase in prediction on top of these routinely available clinical data. Second, we emphasise that many of the candidate biomarkers for which there are numerous sporadic reports in the literature are tightly correlated with each other. Despite this, few studies have attempted to evaluate a wide range of biomarkers simultaneously to define the most useful among these correlated biomarkers. We also review the potential of high-dimensional panels of lipids, metabolites and proteins to advance the field, and point to some of the analytical and post-analytical challenges of taking initial studies using these and candidate approaches through to actual clinical biomarker use.

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria

Background

The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown.

Methods

Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan–Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years.

Results

CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (β = 0.402, p < 0.001) and eGFR (β = − 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model.

Conclusion

A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.

Electronic supplementary material

The online version of this article (10.1186/s12933-018-0697-9) contains supplementary material, which is available to authorized users.

The Promise of Systems Biology for Diabetic Kidney Disease

Diabetic kidney disease (DKD) has a complex and prolonged pathogenesis involving many cell types in the kidney as well as extrarenal factors. It is clinically silent for many years after the onset of diabetes and usually progresses over decades. Given this complexity, a comprehensive and unbiased molecular approach is best suited to help identify the most critical mechanisms responsible for progression of DKD and those most suited for targeted intervention. Systems biological investigations provide such an approach since they examine the entire network of molecular changes that occur in a disease process in a comprehensive way instead of focusing on a single abnormal molecule or pathway. Systems biological studies can also start with analysis of the disease in humans, not in animal or cell culture models that often poorly reproduce the changes in human DKD. Indeed, in the last decade, systems biological approaches have led to the identification of critical molecular abnormalities in DKD and have directly led to development of new biomarkers and potential treatments for DKD.

Albuminuria and neck circumference are determinate factors of successful accurate estimation of glomerular filtration rate in high cardiovascular risk patients

Background

Estimated glomerular filtration rate (eGFR) is used for diagnosis of chronic kidney disease (CKD). The eGFR models based on serum creatinine or cystatin C are used more in clinical practice. Albuminuria and neck circumference are associated with CKD and may have correlations with eGFR.

Aim

We explored the correlations and modelling formulates among various indicators such as serum creatinine, cystatin C, albuminuria, and neck circumference for eGFR.

Design

Cross-sectional study.

Methods

We reviewed the records of patients with high cardiovascular risk from 2010 to 2011 in Taiwan. 24-hour urine creatinine clearance was used as the standard. We utilized a decision tree to select for variables and adopted a stepwise regression method to generate five models. Model 1 was based on only serum creatinine and was adjusted for age and gender. Model 2 added serum cystatin C, models 3 and 4 added albuminuria and neck circumference, respectively. Model 5 simultaneously added both albuminuria and neck circumference.

Results

Total 177 patients were recruited in this study. In model 1, the bias was 2.01 and its precision was 14.04. In model 2, the bias was reduced to 1.86 with a precision of 13.48. The bias of model 3 was 1.49 with a precision of 12.89, and the bias for model 4 was 1.74 with a precision of 12.97. In model 5, the bias could be lower to 1.40 with a precision of 12.53.

Conclusions

In this study, the predicting ability of eGFR was improved after the addition of serum cystatin C compared to serum creatinine alone. The bias was more significantly reduced by the calculation of albuminuria. Furthermore, the model generated by combined albuminuria and neck circumference could provide the best eGFR predictions among these five eGFR models. Neck circumference can be investigated potentially in the further studies.

Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

PURPOSE OF REVIEW

Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years.

RECENT FINDINGS

Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, α-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression.

SUMMARY

Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making.

Association of kidney fibrosis with urinary peptides: a path towards non-invasive liquid biopsies?

Chronic kidney disease (CKD) is a prevalent cause of morbidity and mortality worldwide. A hallmark of CKD progression is renal fibrosis characterized by excessive accumulation of extracellular matrix (ECM) proteins. In this study, we aimed to investigate the correlation of the urinary proteome classifier CKD273 and individual urinary peptides with the degree of fibrosis. In total, 42 kidney biopsies and urine samples were examined. The percentage of fibrosis per total tissue area was assessed in Masson trichrome stained kidney tissues. The urinary proteome was analysed by capillary electrophoresis coupled to mass spectrometry. CKD273 displayed a significant and positive correlation with the degree of fibrosis (Rho = 0.430, P = 0.0044), while the routinely used parameters (glomerular filtration rate, urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio) did not (Rho = -0.222; -0.137; -0.070 and P = 0.16; 0.39; 0.66, respectively). We identified seven fibrosis-associated peptides displaying a significant and negative correlation with the degree of fibrosis. All peptides were collagen fragments, suggesting that these may be causally related to the observed accumulation of ECM in the kidneys. CKD273 and specific peptides are significantly associated with kidney fibrosis; such an association could not be detected by other biomarkers for CKD. These non-invasive fibrosis-related biomarkers can potentially be implemented in future trials.

The effect of sodium/glucose cotransporter 2 (SGLT2) inhibition on the urinary proteome

Treatment with empagliflozin, an inhibitor of the sodium/glucose cotransporter 2 (SGLT2), is associated with slower progression of diabetic kidney disease. In this analysis, we explored the hypothesis that empagliflozin may have an impact on urinary peptides associated with chronic kidney disease (CKD). In this post-hoc, exploratory analysis, we investigated urine samples obtained from 40 patients with uncomplicated type 1 diabetes (T1D) before and after treatment with empagliflozin for 8 weeks to for significant post-therapy changes in urinary peptides. We further assessed the association of these changes with CKD in an independent cohort, and with a previously established urinary proteomic panel, termed CKD273. 107 individual peptides significantly changed after treatment. The majority of the empagliflozin-induced changes were in the direction of “CKD absent” when compare to patients with CKD and controls. A classifier consisting of these 107 peptides scored significantly different in controls, in comparison to CKD patients. However, empagliflozin did not impact the CKD273 classifier. Our data indicate that empagliflozin induces multiple significant changes in the urinary proteomic markers such as mucin and clusterin. The relationship between empagliflozin-induced proteomic changes and clinical outcomes merits further investigation.

Establishing the presence or absence of chronic kidney disease: Uses and limitations of formulas estimating the glomerular filtration rate

The development of formulas estimating glomerular filtration rate (eGFR) from serum creatinine and cystatin C and accounting for certain variables affecting the production rate of these biomarkers, including ethnicity, gender and age, has led to the current scheme of diagnosing and staging chronic kidney disease (CKD), which is based on eGFR values and albuminuria. This scheme has been applied extensively in various populations and has led to the current estimates of prevalence of CKD. In addition, this scheme is applied in clinical studies evaluating the risks of CKD and the efficacy of various interventions directed towards improving its course. Disagreements between creatinine-based and cystatin-based eGFR values and between eGFR values and measured GFR have been reported in various cohorts. These disagreements are the consequence of variations in the rate of production and in factors, other than GFR, affecting the rate of removal of creatinine and cystatin C. The disagreements create limitations for all eGFR formulas developed so far. The main limitations are low sensitivity in detecting early CKD in several subjects, e.g., those with hyperfiltration, and poor prediction of the course of CKD. Research efforts in CKD are currently directed towards identification of biomarkers that are better indices of GFR than the current biomarkers and, particularly, biomarkers of early renal tissue injury.

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET)-Rationale and database description

OBJECTIVES

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET; NCT03152422) aims: (i) to construct new multidimensional urinary proteomic (UP) classifiers that after heart transplantation (HTx) help in detecting graft vasculopathy, monitoring immune system activity and graft performance, and in adjusting immunosuppression; (ii) to sequence UP peptide fragments and to identify key proteins mediating HTx-related complications; (iii) to validate UP classifiers by demonstrating analogy between UP profiles and tissue proteomic signatures (TP) in diseased explanted hearts, to be compared with normal donor hearts; (iv) and to identify new drug targets. This article describes the uPROPHET database construction, follow-up strategies and baseline characteristics of the HTx patients.

METHODS

HTx patients enrolled at the University Hospital Gasthuisberg (Leuven) collected mid-morning urine samples. Cardiac biopsies were obtained at HTx. UP and TP methods and the statistical work flow in pursuit of the research objectives are described in detail in the Data supplement.

RESULTS

Of 352 participants in the UP study (24.4% women), 38.9%, 40.3%, 5.7% and 15.1% had ischemic, dilated, hypertrophic or other cardiomyopathy. The median interval between HTx and first UP assessment (baseline) was 7.8 years. At baseline, mean values were 56.5 years for age, 25.2 kg/m2 for body mass index, 142.3/84.8 mm Hg and 124.2/79.8 mm Hg for office and 24-h ambulatory systolic/diastolic pressure, and 58.6 mL/min/1.73 m2 for the estimated glomerular filtration rate. Of all patients, 37.2% and 6.5% had a history of mild (grade = 1B) or severe (grade ≥ 2) cellular rejection. Anti-body mediated rejection had occurred in 6.2% patients. The number of follow-up urine samples available for future analyses totals over 950. The TP study currently includes biopsies from 7 healthy donors and 15, 14, and 3 patients with ischemic, dilated, and hypertrophic cardiomyopathy.

CONCLUSIONS

uPROPHET constitutes a solid resources for UP and TP research in the field of HTx and has the ambition to lay the foundation for the clinical application of UP in risk stratification in HTx patients.

Use of Proteomics To Investigate Kidney Function Decline over 5 Years

BACKGROUND AND OBJECTIVES

Using a discovery/replication approach, we investigated associations between a multiplex panel of 80 circulating proteins associated with cardiovascular pathology or inflammation, and eGFR decline per year and CKD incidence.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We used two cohorts, the Prospective Investigation of the Vasculature in Uppsala Seniors Study (PIVUS; n=687, mean age of 70 years, 51% women) and the Uppsala Longitudinal Study of Adult Men (ULSAM; n=360 men, mean age of 78 years), with 5-year follow-up data on eGFR. There were 231 and 206 incident cases of CKD during follow-up in the PIVUS and ULSAM studies, respectively. Proteomic profiling of 80 proteins was assessed by a multiplex assay (proximity extension assay). The assay uses two antibodies for each protein and a PCR step to achieve a high-specific binding and the possibility to measure multiple proteins in parallel, but gives no absolute concentrations.

RESULTS

In the discovery cohort from the PIVUS Study, 28 plasma proteins were significantly associated with eGFR decline per year, taking into account the multiple testing. Twenty of these proteins were significantly associated with eGFR decline per year in the replication cohort from the ULSAM Study after adjustment for age, sex, cardiovascular risk factors, medications, and urinary albumin-to-creatinine ratio (in order of significance: TNF-related apoptosis-inducing ligand receptor 2*, CD40L receptor, TNF receptor 1*, placenta growth factor*, thrombomodulin*, urokinase plasminogen activator surface receptor*, growth/differentiation factor 15*, macrophage colony-stimulating factor 1, fatty acid-binding protein*, cathepsin D, resistin, kallikrein 11*, C-C motif chemokine 3, proteinase-activated receptor 1*, cathepsin L, chitinase 3-like protein 1, TNF receptor 2*, fibroblast growth factor 23*, monocyte chemotactic protein 1, and kallikrein 6). Moreover, 11 of the proteins predicted CKD incidence (marked with * above). No protein consistently predicted eGFR decline per year independently of baseline eGFR in both cohorts.

CONCLUSIONS

Several circulating proteins involved in phosphate homeostasis, inflammation, apoptosis, extracellular matrix remodeling, angiogenesis, and endothelial dysfunction were associated with worsening kidney function. Multiplex proteomics appears to be a promising way of discovering novel aspects of kidney disease pathology.

Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome

In its early stages, symptoms of chronic kidney disease (CKD) are usually not apparent. Significant reduction of the kidney function is the first obvious sign of disease. If diagnosed early (stages 1 to 3), the progression of CKD can be altered and complications reduced. In stages 4 and 5 extensive kidney damage is observed, which usually results in end-stage renal failure. Currently, the diagnosis of CKD is made usually on the levels of blood urea and serum creatinine (sCr), however, sCr has been shown to be lacking high predictive value. Due to the development of genomics, epigenetics, transcriptomics, proteomics, and metabolomics, the introduction of novel techniques will allow for the identification of novel biomarkers in renal diseases. This review presents some new possible biomarkers in the diagnosis of CKD and in the prediction of outcome, including asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), uromodulin, kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), miRNA, ncRNA, and lincRNA biomarkers and proteomic and metabolomic biomarkers. Complicated pathomechanisms of CKD development and progression require not a single marker but their combination in order to mirror all types of alterations occurring in the course of this disease. It seems that in the not so distant future, conventional markers may be exchanged for new ones, however, confirmation of their efficacy, sensitivity and specificity as well as the reduction of analysis costs are required.

Clinical proteomics in kidney disease as an exponential technology: heading towards the disruptive phase

Exponential technologies double in power or processing speed every year, whereas their cost halves. Deception and disruption are two key stages in the development of exponential technologies. Deception occurs when, after initial introduction, technologies are dismissed as irrelevant, while they continue to progress, perhaps not as fast or with so many immediate practical applications as initially thought. Twenty years after the first publications, clinical proteomics is still not available in most hospitals and some clinicians have felt deception at unfulfilled promises. However, there are indications that clinical proteomics may be entering the disruptive phase, where, once refined, technologies disrupt established industries or procedures. In this regard, recent manuscripts in CKJ illustrate how proteomics is entering the clinical realm, with applications ranging from the identification of amyloid proteins in the pathology lab, to a new generation of urinary biomarkers for chronic kidney disease (CKD) assessment and outcome prediction. Indeed, one such panel of urinary peptidomics biomarkers, CKD273, recently received a Food and Drug Administration letter of support, the first ever in the CKD field. In addition, a must-read resource providing information on kidney disease-related proteomics and systems biology databases and how to access and use them in clinical decision-making was also recently published in CKJ.

Urinary peptide-based classifier CKD273: towards clinical application in chronic kidney disease

Capillary electrophoresis coupled with mass spectrometry (CE-MS) has been used as a platform for discovery and validation of urinary peptides associated with chronic kidney disease (CKD). CKD affects ∼ 10% of the population, with high associated costs for treatments. A urinary proteome-based classifier (CKD273) has been discovered and validated in cross-sectional and longitudinal studies to assess and predict the progression of CKD. It has been implemented in studies employing cohorts of > 1000 patients. CKD273 is commercially available as an in vitro diagnostic test for early detection of CKD and is currently being used for patient stratification in a multicentre randomized clinical trial (PRIORITY). The validity of the CKD273 classifier has recently been evaluated applying the Oxford Evidence-Based Medicine and Southampton Oxford Retrieval Team guidelines and a letter of support for CKD273 was issued by the US Food and Drug Administration. In this article we review the current evidence published on CKD273 and the challenges associated with implementation. Definition of a possible surrogate early endpoint combined with CKD273 as a biomarker for patient stratification currently appears as the most promising strategy to enable the development of effective drugs to be used at an early time point when intervention can still be effective.

Urine biomarkers of tubular injury do not improve on the clinical model predicting chronic kidney disease progression

Few investigations have evaluated the incremental usefulness of tubular injury biomarkers for improved prediction of chronic kidney disease (CKD) progression. As such, we measured urinary kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, N-acetyl-ß-D-glucosaminidase and liver fatty acid binding protein under highly standardized conditions among 2466 enrollees of the prospective Chronic Renal Insufficiency Cohort Study. During 9433 person-years of follow-up, there were 581 cases of CKD progression defined as incident end-stage renal disease or halving of the estimated glomerular filtration rate. Levels of the urine injury biomarkers, normalized for urine creatinine, were strongly associated with CKD progression in unadjusted Cox proportional hazard models with hazard ratios in the range of 7 to 15 comparing the highest with the lowest quintiles. However, after controlling for the serum creatinine-based estimated glomerular filtration rate and urinary albumin/creatinine ratio, none of the normalized biomarkers was independently associated with CKD progression. None of the biomarkers improved on the high (0.89) C-statistic for the base clinical model. Thus, among patients with CKD, risk prediction with a clinical model that includes the serum creatinine-based estimated glomerular filtration rate and the urinary albumin/creatinine ratio is not improved on with the addition of renal tubular injury biomarkers.

Toward Noninvasive Diagnosis of IgA Nephropathy: A Pilot Urinary Metabolomic and Proteomic Study

IgA nephropathy is diagnosed by renal biopsy, an invasive procedure with a risk of significant complications. Noninvasive approaches are needed for possible diagnostic purposes and especially for monitoring disease activity or responses to treatment. In this pilot project, we assessed the utility of urine samples as source of biomarkers of IgA nephropathy. We used spot urine specimens from 19 healthy controls, 11 patients with IgA nephropathy, and 8 renal-disease controls collected on day of renal biopsy. Urine samples were analyzed using untargeted metabolomic and targeted proteomic analyses by several experimental techniques: liquid chromatography coupled with mass spectrometry, immunomagnetic isolation of target proteins coupled with quantitation by mass spectrometry, and protein arrays. No single individual biomarker completely differentiated the three groups. Therefore, we tested the utility of several markers combined in a panel. Discriminant analysis revealed that combination of seven markers, three metabolites (dodecanal, 8-hydroxyguanosine, and leukotriene C₄), three proteins (α1-antitrypsin, IgA-uromodulin complex, and galactose-deficient IgA1), and heparan sulfate, differentiated patients with IgA nephropathy from patients with other renal diseases and healthy controls. Future studies are needed to validate these preliminary findings and to determine the power of these urinary markers for assessment of responses to therapy.